Tree-based iterated local search for Markov random fields with applications in image analysis
The maximum a posteriori assignment for general structure Markov random fields is computationally intractable. In this paper, we exploit tree-based methods to efficiently address this problem. Our novel method, named Tree-based Iterated Local Search (T-ILS), takes advantage of the tractability of tr...
| Main Authors: | , , |
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| Format: | Journal Article |
| Published: |
Kluwer Academic Publishers
2014
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| Online Access: | http://hdl.handle.net/20.500.11937/39305 |
| _version_ | 1848755555229761536 |
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| author | Tran, The Truyen Phung, D. Venkatesh, S. |
| author_facet | Tran, The Truyen Phung, D. Venkatesh, S. |
| author_sort | Tran, The Truyen |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | The maximum a posteriori assignment for general structure Markov random fields is computationally intractable. In this paper, we exploit tree-based methods to efficiently address this problem. Our novel method, named Tree-based Iterated Local Search (T-ILS), takes advantage of the tractability of tree-structures embedded within MRFs to derive strong local search in an ILS framework. The method efficiently explores exponentially large neighborhoods using a limited memory without any requirement on the cost functions. We evaluate the T-ILS on a simulated Ising model and two real-world vision problems: stereo matching and image denoising. Experimental results demonstrate that our methods are competitive against state-of-the-art rivals with significant computational gain. |
| first_indexed | 2025-11-14T08:58:10Z |
| format | Journal Article |
| id | curtin-20.500.11937-39305 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T08:58:10Z |
| publishDate | 2014 |
| publisher | Kluwer Academic Publishers |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-393052018-03-29T09:07:34Z Tree-based iterated local search for Markov random fields with applications in image analysis Tran, The Truyen Phung, D. Venkatesh, S. The maximum a posteriori assignment for general structure Markov random fields is computationally intractable. In this paper, we exploit tree-based methods to efficiently address this problem. Our novel method, named Tree-based Iterated Local Search (T-ILS), takes advantage of the tractability of tree-structures embedded within MRFs to derive strong local search in an ILS framework. The method efficiently explores exponentially large neighborhoods using a limited memory without any requirement on the cost functions. We evaluate the T-ILS on a simulated Ising model and two real-world vision problems: stereo matching and image denoising. Experimental results demonstrate that our methods are competitive against state-of-the-art rivals with significant computational gain. 2014 Journal Article http://hdl.handle.net/20.500.11937/39305 10.1007/s10732-014-9270-1 Kluwer Academic Publishers restricted |
| spellingShingle | Tran, The Truyen Phung, D. Venkatesh, S. Tree-based iterated local search for Markov random fields with applications in image analysis |
| title | Tree-based iterated local search for Markov random fields with applications in image analysis |
| title_full | Tree-based iterated local search for Markov random fields with applications in image analysis |
| title_fullStr | Tree-based iterated local search for Markov random fields with applications in image analysis |
| title_full_unstemmed | Tree-based iterated local search for Markov random fields with applications in image analysis |
| title_short | Tree-based iterated local search for Markov random fields with applications in image analysis |
| title_sort | tree-based iterated local search for markov random fields with applications in image analysis |
| url | http://hdl.handle.net/20.500.11937/39305 |